Global Ozone Monitoring Experiment Research Articles

Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001-2018).

Tropospheric nitrogen dioxide (NO2) poses a serious threat to the environmental quality and public health. Satellite NO2 observations have been continuously used to monitor NO2 variations and improve model performances. However, the accuracy of satellite NO2 retrieval depends on the knowledge of aerosol optical properties, in particular for urban agglomerations accompanied by significant changes in aerosol characteristics. In this study, we investigate the impacts of aerosol composition on tropospheric NO2 retrieval for an 18 year global data set from Global Ozone Monitoring Experiment (GOME)-series satellite sensors. With a focus on cloud-free scenes dominated by the presence of aerosols, individual aerosol composition affects the uncertainties of tropospheric NO2 columns through impacts on the aerosol loading amount, relative vertical distribution of aerosol and NO2, aerosol absorption properties, and surface albedo determination. Among aerosol compositions, secondary inorganic aerosol mostly dominates the NO2 uncertainty by up to 43.5% in urban agglomerations, while organic aerosols contribute significantly to the NO2 uncertainty by -8.9 to 37.3% during biomass burning seasons. The possible contrary influences from different aerosol species highlight the importance and complexity of aerosol correction on tropospheric NO2 retrieval and indicate the need for a full picture of aerosol properties. This is of particular importance for interpreting seasonal variations or long-term trends of tropospheric NO2 columns as well as for mitigating ozone and fine particulate matter pollution.

Investigation of the summer 2018 European ozone air pollution episodes using novel satellite data and modelling

Abstract. In the summer of 2018, Europe experienced an intense heatwave which coincided with several persistent large-scale ozone (O3) pollution episodes. Novel satellite data of lower-tropospheric column O3 from the Global Ozone Monitoring Experiment-2 (GOME-2) and Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp satellite showed substantial enhancements in 2018 relative to other years since 2012. Surface observations also showed ozone enhancements across large regions of continental Europe in summer 2018 compared to 2017. Enhancements to surface temperature and the O3 precursor gases carbon monoxide and methanol in 2018 were co-retrieved from MetOp observations by the same scheme. This analysis was supported by the TOMCAT chemistry transport model (CTM) to investigate processes driving the observed O3 enhancements. Through several targeted sensitivity experiments we show that meteorological processes, and emissions to a secondary order, were important for controlling the elevated O3 concentrations at the surface. However, mid-tropospheric (∼ 500 hPa) O3 enhancements were dominated by meteorological processes. We find that contributions from stratospheric O3 intrusions ranged between 15 %–40 %. Analysis of back trajectories indicates that the import of O3-enriched air masses into Europe originated over the North Atlantic, substantially increasing O3 in the 500 hPa layer during summer 2018.

Record Low Arctic Stratospheric Ozone in Spring 2020: Measurements of Ground-Based Differential Optical Absorption Spectroscopy in Ny-Ålesund during 2017–2021

The Arctic stratospheric ozone depletion event in spring 2020 was the most severe compared with previous years. We retrieved the critical indicator ozone vertical column density (VCD) using zenith scattered light differential optical absorption spectroscopy (ZSL-DOAS) from March 2017 to September 2021 in Ny-Ålesund, Svalbard, Norway. The average ozone VCD over Ny-Ålesund between 18 March and 18 April 2020 was approximately 274.8 Dobson units (DU), which was only 64.7 ± 0.1% of that recorded in other years (2017, 2018, 2019, and 2021). The daily peak difference was 195.7 DU during this period. The retrieved daily averages of ozone VCDs were compared with satellite observations from the Global Ozone Monitoring Experiment-2 (GOME-2), a Brewer spectrophotometer, and a Système d’Analyze par Observation Zénithale (SAOZ) spectrometer at Ny-Ålesund. As determined using the empirical cumulative density function, ozone VCDs from the ZSL-DOAS dataset were strongly correlated with data from the GOME-2 and SAOZ at lower and higher values, and ozone VCDs from the Brewer instrument were overestimated. The resulting Pearson correlation coefficients were relatively high at 0.97, 0.87, and 0.91, respectively. In addition, the relative deviations were 2.3%, 3.1%, and 3.5%, respectively. Sounding and ERA5 data indicated that severe ozone depletion occurred between mid-March and mid-April 2020 in the 16–20 km altitude range over Ny-Ålesund, which was strongly associated with the overall persistently low temperatures in the winter of 2019/2020. Using ZSL-DOAS observations, we obtained ozone VCDs and provided evidence for the unprecedented ozone depletion during the Arctic spring of 2020. This is essential for the study of polar ozone changes and their effect on climate change and ecological conditions.

Impact of different sources of precursors on an ozone pollution outbreak over Europe analysed with IASI+GOME2 multispectral satellite observations and model simulations

Abstract. We examine the impact of different sources of ozone precursors on the daily evolution of successive ozone pollution outbreaks across Europe in July 2017 by using a multispectral satellite approach called IASI+GOME2 and a tropospheric chemistry reanalysis named TCR-2. IASI+GOME2, combining IASI (Infrared Atmospheric Sounding Interferometer) and GOME-2 (Global Ozone Monitoring Experiment-2) measurements respectively in the infrared and the ultraviolet, allows the observation of the daily horizontal distribution of ozone in the lowermost troposphere (defined here as the atmospheric layer between the surface and 3 km above sea level). IASI+GOME2 observations show a fair capacity to depict near-surface ozone evolution as compared to surface measurements from 188 European stations for the period 15–27 July 2017. At the beginning of this event (on 16 July), an ozone outbreak is initially formed over the Iberian Peninsula likely linked with high temperature-induced enhancements of biogenic volatile organic compound concentrations and collocated anthropogenic emissions. In the following days, the ozone plume splits into two branches, one being transported eastward across the western Mediterranean and Italy and the other one over western and Central Europe. The southern branch encounters ozone precursors emitted over the Balkan Peninsula by wildfires along the coast of the Adriatic Sea and biogenic sources in the inland region of the peninsula. Ozone concentrations of the northern plume are enhanced by photochemical production associated with anthropogenic sources of ozone precursors over Central Europe and by mixing with an ozone plume arriving from the North Sea that was originally produced over North America. Finally, both ozone branches are transported eastwards and mix gradually as they reach the northern coast of the Black Sea. There, emissions from agricultural fires after harvesting clearly favour photochemical production of ozone within the pollution plume, which is advected eastwards in the following days. Based on satellite analysis, this paper shows the interplay of various ozone precursor sources to sustain a 2-week-long ozone pollution event over different parts of Europe.

Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products of atmospheric trace gas columns

Abstract. We introduce the new Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 product of total column ozone (O3), total and tropospheric column nitrogen dioxide (NO2), total column water vapour, total column bromine oxide (BrO), total column formaldehyde (HCHO), and total column sulfur dioxide (SO2) (daily products https://doi.org/10.15770/EUM_SAF_AC_0048, AC SAF, 2023a; monthly products https://doi.org/10.15770/EUM_SAF_AC_0049, AC SAF, 2023b). The GOME-2 level-3 products aim to provide easily translatable and user-friendly data sets to the scientific community for scientific progress as well as to satisfy public interest. The purpose of this paper is to present the theoretical basis as well as the verification and validation of the GOME-2 daily and monthly level-3 products. The GOME-2 level-3 products are produced using the overlapping area-weighting method. Details of the gridding algorithm are presented. The spatial resolution of the GOME-2 level-3 products is selected based on the sensitivity study. The consistency of the resulting level-3 products among three GOME-2 sensors is investigated through time series of global averages, zonal averages, and bias. The accuracy of the products is validated by comparison to ground-based observations. The verification and validation results show that the GOME-2 level-3 products are consistent with the level-2 data. Small discrepancies are found among three GOME-2 sensors, which are mainly caused by the differences in the instrument characteristic and level-2 processor. The comparison of GOME-2 level-3 products to ground-based observations in general shows very good agreement, indicating that the products are consistent and fulfil the requirements to serve the scientific community and general public.

Investigation of three-dimensional radiative transfer effects for UV–Vis satellite and ground-based observations of volcanic plumes

Abstract. We investigate effects of the three-dimensional (3D) structure of volcanic plumes on the retrieval results of satellite and ground-based UV–Vis observations. For the analysis of such measurements, 1D scenarios are usually assumed (the atmospheric properties only depend on altitude). While 1D assumptions are well suited for the analysis of many atmospheric phenomena, they are usually less appropriate for narrow trace gas plumes. For UV–Vis satellite instruments with large ground pixel sizes like the Global Ozone Monitoring Experiment-2 (GOME-2), the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) or the Ozone Monitoring Instrument (OMI), 3D effects are of minor importance, but usually these observations are not sensitive to small volcanic plumes. In contrast, observations of the TROPOspheric Monitoring Instrument (TROPOMI) on board Sentinel-5P have a much smaller ground pixel size (3.5 × 5.5 km2). Thus, on the one hand, TROPOMI can detect much smaller plumes than previous instruments. On the other hand, 3D effects become more important, because the TROPOMI ground pixel size is smaller than the height of the troposphere and also smaller than horizontal atmospheric photon path lengths in the UV–Vis spectral range. In this study we investigate the following 3D effects using Monte Carlo radiative transfer simulations: (1) the light-mixing effect caused by horizontal photon paths, (2) the saturation effect for strong SO2 absorption, (3) geometric effects related to slant illumination and viewing angles and (4) plume side-effects related to slant illumination angles and photons reaching the sensor from the sides of volcanic plumes. The first two effects especially can lead to a strong and systematic underestimation of the true trace gas content if 1D retrievals are applied (more than 50 % for the light-mixing effect and up to 100 % for the saturation effect). Besides the atmospheric radiative transfer, the saturation effect also affects the spectral retrievals. Geometric effects have a weaker influence on the quantitative analyses but can lead to a spatial smearing of elevated plumes or even to virtual double plumes. Plume side-effects are small for short wavelengths but can become large for longer wavelengths (up to 100 % for slant viewing and illumination angles). For ground-based observations, most of the above-mentioned 3D effects are not important because of the narrow field of view (FOV) and the closer distance between the instrument and the volcanic plume. However, the light-mixing effect shows a similar strong dependence on the horizontal plume extension as for satellite observations and should be taken into account for the analysis of ground-based observations.

Trends and variability of total column ozone in the Third Pole

The Hindu Kush Himalaya and Tien Shan Mountain regions together are called the Third Pole (TP) of Earth, which encompasses ecologically fragile regions of 12 Asian countries. It is the highest mountain chain with the largest reserve of fresh ice mass on the planet outside the northern and southern polar regions. The TP region is experiencing high rate of glacier melting due to climate change for the past few decades, and is a great concern for water security of South Asia. Since changes in ozone concentrations in the atmosphere affect public health, ecosystem dynamics and climate, it is imperative to monitor its temporal evolution in an ecologically sensitive region such as TP. Here, the spatiotemporal characteristics of total column ozone (TCO) in TP and 20 selected cities in and around TP are investigated using a combined long-term data made from the satellite measurements of Ozone Monitoring Instrument (OMI) and Global Ozone Monitoring Experiment (GOME)-2B for the period 2005–2020. The spatial trends in TCO over TP are mostly negative in summer and autumn (from −0.2 DU/yr to −0.6 DU/yr), but positive in winter (up to +0.2 DU/yr). Among the selected 20 urban regions, the highest annual trend −0.42 ± 0.3 DU/yr and the lowest −0.01 ± 0.2 DU/yr are estimated in Xining and Chittagong, respectively. Analysis using a multiple regression model reveals that the ozone variability in TP is mostly driven by tropopause height with a contribution of 24.92%, Quasi-Biennial Oscillation (23.42%), aerosols (16.12%) and solar flux (15.34%). Our study suggests that the observed negative trend is mainly associated with human activities and climate change in TP, which would likely to enhance the surface temperature and thus, melting of glaciers in the region.

Enterprise version 8 total column ozone algorithm (EV8TOz) development and applications on multiple sensors

ABSTRACT We expand the Version 8 Total Column Ozone algorithm (V8TOz) into an Enterprise V8TOz (EV8TOz), which provides a unique platform accommodating a variety of UV/Visible satellite measurements for total ozone (TOz) estimation. A satellite-oriented flexible dynamic input data space applicable to any existing and upcoming satellite UV/Visible sensor measurements is developed in EV8TOz. The EV8TOz also provides flexible choices of band adjustment for either conducting sensor original single band or combined broad band retrievals, and includes options for introducing soft calibration adjustments. The applications of EV8TOz to the Ozone Mapping and Profiler Suite (OMPS) aboard Suomi National Polar-orbiting Partnership (NPP) and NOAA-20 (N20), Global Ozone Monitoring Experiment-2 (GOME-2) aboard Meteorological Operational satellite (Metop)-B and MetOp-C and the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor (S5P) are demonstrated. As far as we know, it is the first time TROPOMI ozone has been estimated and soft-calibrated with this limited wavelength channels and table lookup technique for the V8TOz. Statistical analyses show that EV8TOz estimated global TOz with measurements from these five sensors on the same day have less than±2% differences relative to the NASA NPP retrievals.

Sun-induced fluorescence as a proxy for primary productivity across vegetation types and climates

Abstract. Sun-induced chlorophyll a fluorescence (SIF) retrieved from satellites has shown potential as a remote sensing proxy for gross primary productivity (GPP). However, to fully exploit the potential of this signal, the robustness and stability of the SIF–GPP relationship across vegetation types and climates must be assessed. For this purpose, current studies have been limited by the availability of SIF datasets with sufficient spatial resolution to disentangle the signal between different vegetation cover types. To overcome this limitation, this analysis uses GOME-2 (Global Ozone Monitoring Experiment-2) SIF retrievals, downscaled to a resolution of 0.05∘ (∼5 km) to explore the relationship between SIF and FLUXCOM GPP (GPPFX), a data-driven dataset of primary productivity obtained by upscaling flux-tower measurements. The high resolution of the downscaled SIF (SIFDS) dataset allows the relationships to be broken down by vegetation cover for separate climate zones, thus enabling a confrontation between GPP and SIF at fine granularity. This analysis first investigates the spatial and temporal relationships between FLUXCOM GPP and downscaled SIF at a global scale. A reasonably strong linear relationship is generally observed between SIFDS and GPPFX in all vegetation categories, and an analysis of covariance (ANCOVA) shows that the spatial response is similar between certain plant traits, with some distinction between herbaceous and woody vegetation and notable exceptions, such as equatorial broadleaf forests. Geographical regions of non-linearity suggest where SIFDS could potentially provide information about ecosystem dynamics that are not represented in the FLUXCOM GPP dataset. With the demonstration of downscaled SIF as a proxy for GPP, the response of SIFDS to short-term fluctuations in several meteorological variables is analysed and the most significant short-term environmental driving and limiting meteorological variables determined. Vegetation groupings of similar SIF–meteorological response reinforce the vegetation categorisations suggested by the ANCOVA. This comparative exploration of two of the most recent products in carbon productivity estimation shows the value in downscaling SIF data, provides an independent probe of the FLUXCOM GPP model, enhances our understanding of the global SIF–GPP spatio-temporal relationship with a particular focus on the role of vegetation cover, and explores the similarity of the SIF and GPP responses to meteorological fluctuations. Additional analyses with alternative SIF and GPP datasets support these conclusions.

Variability of Remotely Sensed Solar-Induced Chlorophyll Fluorescence in Relation to Climate Indices

Global remote sensing of solar-induced fluorescence (SIF), a proxy for plant photosynthetic activity, represents a breakthrough in the systematic observation of global-scale gross primary production and other ecosystem functions. Here, we hypothesize that all earth ecosystem variabilities, including SIF, are affected by climate variations. The main contribution of this study is to apply a global empirical orthogonal function (EOF) analysis of SIF to quantify the relations between the large-scale GPP variability and climate variations. We used 2007–2019 SIF data derived from the Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensor observations and a rotated empirical orthogonal function (EOF) analysis to explore global SIF variability over years and decades. The first leading EOF mode captures the well-known ENSO pattern, with most of the variance over continents in the tropical Pacific and Indian Oceans. The second and third leading EOF modes in SIF variability are significantly related to the NAO and PDO climate indices, respectively. Our analysis also shows that the 2011 La Niña (2015 El Niño) elevated (decreased) global SIF.

Response of dryland vegetation under extreme wet events with satellite measures of greenness and fluorescence

Extreme wet events in central Australia triggered large vegetation responses that contributed greatly to large global land carbon sink anomalies. There remain significant uncertainties on the extent to which these events over dryland vegetation can be monitored and assessed with satellite data. In this study, we investigated the vegetation responses of the major Australian semiarid biomes to two extreme wet events utilizing multi-satellite observations of (1) solar-induced chlorophyll fluorescence (SIF), as a proxy for photosynthetic activity and (2) the enhanced vegetation index (EVI), as a measure of canopy chlorophyll or greenness. We related these satellite observations with gross primary productivity (GPP) estimated from eddy covariance tower sites, as a performance benchmark.The C3-dominated Mulga woodland was the most responsive biome to both wet pulses and exhibited the highest sensitivity to soil moisture. The C4-dominated Hummock grassland was more responsive to the 2011 “big wet” event, relative to the later 2016–2017 wet pulse. EVI swiftly responded to the extreme wet events and showed markedly amplified seasonal amplitude, however, there was a time lag as compared with SIF during the post-wet period, presumably due to the relatively slower chlorophyll degradation in contrast with declines in photosynthetic activity. Despite a robust linear SIF-GPP relationship (r2 ranging from 0.59 to 0.85), the spatially coarse SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) yielded high retrieval noise over the xeric biomes, hindering its capacity to capture thoroughly the dryland vegetation dynamics in central Australia. Our study highlights that synchronous satellite observations of greenness and fluorescence can potentially offer an improved understanding of dryland vegetation dynamics and can advance our ability to detect ecosystem alterations under future changing climates.

Ground-based validation of the MetOp-A and MetOp-B GOME-2 OClO measurements

Abstract. This paper reports on ground-based validation of the atmospheric OClO data record produced within the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC SAF) using the Global Ozone Monitoring Experiment (GOME)-2A and GOME-2B instrument measurements, covering the 2007–2016 and 2013–2016 periods, respectively. OClO slant column densities are compared to correlative measurements collected from nine Zenith-Scattered-Light Differential Optical Absorption Spectroscopy (ZSL-DOAS) instruments from the Network for the Detection of Atmospheric Composition Change (NDACC) distributed in both the Arctic and Antarctic. Sensitivity tests are performed on the ground-based data to estimate the impact of the different OClO DOAS analysis settings. On this basis, we infer systematic uncertainties of about 25 % (i.e., about 3.75×1013 molec. cm−2) between the different ground-based data analyses, reaching total uncertainties ranging from about 26 % to 33 % for the different stations (i.e., around 4 to 5×1013 molec. cm−2). Time series at the different sites show good agreement between satellite and ground-based data for both the inter-annual variability and the overall OClO seasonal behavior. GOME-2A results are found to be noisier than those of GOME-2B, especially after 2011, probably due to instrumental degradation effects. Daily linear regression analysis for OClO-activated periods yield correlation coefficients of 0.8 for GOME-2A and 0.87 for GOME-2B, with slopes with respect to the ground-based data ensemble of 0.64 and 0.72, respectively. Satellite minus ground-based offsets are within 8×1013 molec. cm−2, with some differences between GOME-2A and GOME-2B depending on the station. Overall, considering all the stations, a median offset of about -2.2×1013 molec. cm−2 is found for both GOME-2 instruments.

Global total ozone recovery trends attributed to ozone-depleting substance (ODS) changes derived from five merged ozone datasets

Abstract. We report on updated trends using different merged zonal mean total ozone datasets from satellite and ground-based observations for the period from 1979 to 2020. This work is an update of the trends reported in Weber et al. (2018) using the same datasets up to 2016. Merged datasets used in this study include NASA MOD v8.7 and NOAA Cohesive Data (COH) v8.6, both based on data from the series of Solar Backscatter Ultraviolet (SBUV), SBUV-2, and Ozone Mapping and Profiler Suite (OMPS) satellite instruments (1978–present), as well as the Global Ozone Monitoring Experiment (GOME)-type Total Ozone – Essential Climate Variable (GTO-ECV) and GOME-SCIAMACHY-GOME-2 (GSG) merged datasets (both 1995–present), mainly comprising satellite data from GOME, SCIAMACHY, OMI, GOME-2A, GOME-2B, and TROPOMI. The fifth dataset consists of the annual mean zonal mean data from ground-based measurements collected at the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Trends were determined by applying a multiple linear regression (MLR) to annual mean zonal mean data. The addition of 4 more years consolidated the fact that total ozone is indeed slowly recovering in both hemispheres as a result of phasing out ozone-depleting substances (ODSs) as mandated by the Montreal Protocol. The near-global (60∘ S–60∘ N) ODS-related ozone trend of the median of all datasets after 1995 was 0.4 ± 0.2 (2σ) %/decade, which is roughly a third of the decreasing rate of 1.5 ± 0.6 %/decade from 1978 until 1995. The ratio of decline and increase is nearly identical to that of the EESC (equivalent effective stratospheric chlorine or stratospheric halogen) change rates before and after 1995, confirming the success of the Montreal Protocol. The observed total ozone time series are also in very good agreement with the median of 17 chemistry climate models from CCMI-1 (Chemistry-Climate Model Initiative Phase 1) with current ODS and GHG (greenhouse gas) scenarios (REF-C2 scenario). The positive ODS-related trends in the Northern Hemisphere (NH) after 1995 are only obtained with a sufficient number of terms in the MLR accounting properly for dynamical ozone changes (Brewer–Dobson circulation, Arctic Oscillation (AO), and Antarctic Oscillation (AAO)). A standard MLR (limited to solar, Quasi-Biennial Oscillation (QBO), volcanic, and El Niño–Southern Oscillation (ENSO)) leads to zero trends, showing that the small positive ODS-related trends have been balanced by negative trend contributions from atmospheric dynamics, resulting in nearly constant total ozone levels since 2000.

Assessing the Impact of Extreme Droughts on Dryland Vegetation by Multi-Satellite Solar-Induced Chlorophyll Fluorescence

Satellite-estimated solar-induced chlorophyll fluorescence (SIF) is proven to be an effective indicator for dynamic drought monitoring, while the capability of SIF to assess the variability of dryland vegetation under water and heat stress remains challenging. This study presents an analysis of the responses of dryland vegetation to the worst extreme drought over the past two decades in Australia, using multi-source spaceborne SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) and TROPOspheric Monitoring Instrument (TROPOMI). Vegetation functioning was substantially constrained by this extreme event, especially in the interior of Australia, in which there was hardly seasonal growth detected by neither satellite-based observations nor tower-based flux measurements. At a 16-day interval, both SIF and enhanced vegetation index (EVI) can timely capture the reduction at the onset of drought over dryland ecosystems. The results demonstrate that satellite-observed SIF has the potential for characterizing and monitoring the spatiotemporal dynamics of drought over water-limited ecosystems, despite coarse spatial resolution coupled with high-retrieval noise as compared with EVI. Furthermore, our study highlights that SIF retrieved from TROPOMI featuring substantially enhanced spatiotemporal resolution has the promising capability for accurately tracking the drought-induced variation of heterogeneous dryland vegetation.

TROPOspheric Monitoring Instrument observations of total column water vapour: Algorithm and validation

In this paper, we present the total column water vapour (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the visible blue spectral band. The TROPOMI TCWV algorithm is being optimized and validated in the framework of the Sentinel 5 Precursor Product Algorithm Laboratory (S5P-PAL) project from the European Space Agency (ESA). The retrieval was first developed to retrieve TCWV from the Global Ozone Monitoring Experiment 2 (GOME-2). We have optimized the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV algorithm follows the typical two step approach, using spectral fit retrieval of slant columns, and conversion of the slant columns to vertical columns using air mass factors (AMFs). An iterative optimization algorithm is developed to dynamically find the optimal a priori water vapour profile for the AMF calculation. Further optimizations on the spectral retrieval, air mass factor calculations as well as a new surface albedo retrieval approach are implemented.The TCWV retrieval algorithm is applied to TROPOMI observations from May 2018 to May 2021. The results are validated by comparing them to ERA5 reanalysis data, GOME-2, MODerate resolution Imaging Spectroradiometer (MODIS) and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.96 to 0.99. The mean bias between TROPOMI and ERA5 data is −1.24 kg m−2 for measurements over land and 0.73 kg m−2 for measurements over water. The comparison to MODIS observations show similar results with small dry bias of 1.51,kg m−2 for measurements over land and a small wet bias of 1.25 kg m−2 for measurements over water. Slightly larger dry bias of 1.98 kg m−2 for measurements over land and 1.74 kg m−2 for measurements over water are found when compared to GOME-2 obserations. Compared to SSMIS data over water, TROPOMI observations are bias low by 3.25 kg m−2. The small discrepancies found between TROPOMI and reference data sets are related to the differences in measurement technique, measurement time, surface albedo issue, as well as cloud and aerosol contamination. This study demonstrates that the algorithm can provide stable and consistent results on a global scale and can be applied to generate operational TCWV products from TROPOMI and the forthcoming Copernicus missions Sentinel-4 and Sentinel-5. We have also demonstrated the capability of retrieving fine scale water vapour structures in a case study over the Amazon. This indicates that the TROPOMI data set is also useful for local and regional climate studies.

Characterization and Correction of Intersensor Calibration Convolution Errors Between S-NPP OMPS Nadir Mapper and Metop-B GOME-2

This article introduces a method to correct intersensor calibration convolution errors that occur in the convolution of spectral response functions (SRFs) between narrow-band and broad-band instruments. By using the intersensor calibration analysis between Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) and Global Ozone Monitoring Experiment-2 (GOME-2) as an example, the root cause of convolution errors in the intersensor calibration is addressed through direct comparison of OMPS NM SRF and convolved OMPS SRF with GOME-2 SRF. The results reveal that distorted SRF of the narrow-band instrument is the major cause, which appears for GOME-2 at a wide range of channels. The convolution errors in reflectance, which were ignored in previous studies, can be greater than 2% for wavelength shorter than 320 nm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 0.5$ </tex-math></inline-formula> % for wavelengths between 320 and 330 nm. This study thus presents a hybrid convolution error correction method that consists of theoretical approximation of the convolution errors and empirical estimates of residuals due to the deviation of the theoretical approximation from the actual convolution errors. According to the validation through simulation, after applying convolution error correction, the mean convolution errors are less than 0.02%, while the root mean square errors are reduced from more than 0.5% to less than 0.1%. In addition, the correction method is applied to the intersensor calibration radiometric bias assessment between the Meteorological Operational satellite–B (Metop-B) GOME-2 and the Suomi National Polar-orbiting Partnership (S-NPP) OMPS NM. The averaged intersensor calibration reflectance differences are decreased by more than 16% after convolution error correction.

Exploring the superiority of solar-induced chlorophyll fluorescence data in predicting wheat yield using machine learning and deep learning methods

Reliable forecasts of large-scale wheat yield are very important for global food security. Although solar-induced chlorophyll fluorescence (SIF) is more sensitive than traditional remotely sensed vegetation indices to photosynthetic capacity, the performance of SIF in wheat yield prediction should be further explored. In this study, five satellite variables (i.e., Global Ozone Monitoring Experiment-2 (GOME-2) SIF at 0.5° spatial resolution, global spatially contiguous SIF (CSIF) at 0.05° resolution, and three vegetation indices at 1 km resolution) from 2007 to 2018 were used to predict wheat yield using two linear regression methods (least absolute shrinkage and selection operator regression (LASSO) and ridge regression (RIDGE)), three machine learning methods (support vector regression (SVR), random forest regression (RF), and extreme gradient boosting (XGBoost)), and one deep learning method (long short-term memory (LSTM)) to predict wheat yield across the Indo-Gangetic Plains. The results showed that machine learning and deep learning methods outperformed the two linear regression methods in predicting wheat yield, while the LSTM did not perform better than SVR. The prediction using the high-resolution SIF product had better performance than that using the coarse-resolution SIF product among all years. Moreover, the high-resolution SIF had better performance than the three vegetation indices in yield prediction in 2010, which indicated that the SIF data had great superiority in predicting wheat yield under extreme weather events. Our findings highlight that developing high-quality SIF products in the future has the potential to improve crop yield predictions, and our method can predict wheat yield simply and effectively in cropping areas with limited data.

Glyoxal tropospheric column retrievals from TROPOMI – multi-satellite intercomparison and ground-based validation

Abstract. We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite. Atmospheric glyoxal results from the oxidation of other non-methane volatile organic compounds (NMVOCs) and from direct emissions caused by combustion processes. Therefore, this product is a useful indicator of VOC emissions. It is generated with an improved version of the BIRA-IASB scientific retrieval algorithm relying on the differential optical absorption spectroscopy (DOAS) approach. Among the algorithmic updates, the DOAS fit now includes corrections to mitigate the impact of spectral misfits caused by scene brightness inhomogeneity and strong NO2 absorption. The product comes along with a full error characterization, which allows for providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1 ×1014–3 ×1014 molec. cm−2 (∼30 %–70 % in emission regimes) and originate mostly from a priori data uncertainties and spectral interferences with other absorbing species. The latter may be at the origin, at least partly, of an enhanced glyoxal signal over equatorial oceans, and further investigation is needed to mitigate them. Random errors are large (&gt;6×1014 molec. cm−2) but can be reduced by averaging observations in space and/or time. Benefiting from a high signal-to-noise ratio and a large number of small-size observations, TROPOMI provides glyoxal tropospheric column fields with an unprecedented level of detail. Using the same retrieval algorithmic baseline, glyoxal column data sets are also generated from the Ozone Monitoring Instrument (OMI) on Aura and from the Global Ozone Monitoring Experiment-2 (GOME-2) on board Metop-A and Metop-B. Those four data sets are intercompared over large-scale regions worldwide and show a high level of consistency. The satellite glyoxal columns are also compared to glyoxal columns retrieved from ground-based Multi-AXis DOAS (MAX-DOAS) instruments at nine stations in Asia and Europe. In general, the satellite and MAX-DOAS instruments provide consistent glyoxal columns both in terms of absolute values and variability. Correlation coefficients between TROPOMI and MAX-DOAS glyoxal columns range between 0.61 and 0.87. The correlation is only poorer at one mid-latitude station, where satellite data appear to be biased low during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences of less than 1×1014 molec. cm−2. A larger bias is identified at two sites where the MAX-DOAS columns are very large. Despite this systematic bias, the consistency of the satellite and MAX-DOAS glyoxal seasonal variability is high.

Use of Satellite Data for Air Pollution Modeling in Bulgaria

Air pollution continues to be of concern for Bulgarian cities, mainly due to particulate matter of aerodynamic diameter smaller than 10 μm (PM10). There is public and expert interest in the improvement of two operational air quality modeling systems: the Bulgarian Chemical Weather Forecast System (BgCWFS) and the Local Air Quality Management System (LAQMS) for the city of Plovdiv. The aim of the study is to investigate the effects of satellite data assimilation in BgCWFS on surface concentrations over Bulgaria (resolution 9 km), to downscale BgCWFS output to LAQMS (resolution 250 m), and to examine effects on PM10 in Plovdiv. Data from the Global Ozone Monitoring Experiment-2 (GOME-2) (MetOP satellites) for aerosols, nitrogen dioxide (NO2), and sulfur dioxide (SO2) were assimilated in BgCWFS using objective analysis. Simulation experiments with and without satellite data were conducted for a summer and a winter month. The comparison to surface observations in the country showed improvement of results when using satellite data, especially in the summer due to mineral dust events captured by satellites. The decrease in the normalized mean bias (NMB) over the two months was 43% (PM10) and 73% (SO2). The LAQMS estimated background contributions to PM10 in the city as 32%. The absolute NMB by LAQMS decreased by 38%.

UV-Indien network: ground-based measurements dedicated to the monitoring of UV radiation over the western Indian Ocean

Abstract. Within the framework of the UV-Indien network, nine ground stations have been equipped with ultraviolet broadband radiometers, five of them have also been equipped with an all-sky camera, and the main station in Saint-Denis de la Réunion is also equipped with a spectroradiometer. These stations are spatially distributed to cover a wide range of latitudes, longitudes, altitudes, and environmental conditions in five countries of the western Indian Ocean region (Comoros, France, Madagascar, Mauritius, and Seychelles), a part of the world where almost no measurements have been made so far. The distribution of the stations is based on the scientific interest of studying ultraviolet radiation not only in relation to atmospheric processes but also in order to provide data relevant to fields such as biology, health (prevention of skin cancer), and agriculture. The main scientific objectives of this network are to study the annual and inter-annual variability in the ultraviolet (UV) radiation in this area, to validate the output of numerical models and satellite estimates of ground-based UV measurements, and to monitor UV radiation in the context of climate change and projected ozone depletion in this region. A calibration procedure including three types of calibrations responding to the various constraints of sustaining the network has been put in place, and a data processing chain has been set up to control the quality and the format of the files sent to the various data centres. A method of clear-sky filtering of the data is also applied. Here, we present an intercomparison with other datasets, as well as several daily or monthly representations of the UV index (UVI) and cloud fraction data, to discuss the quality of the data and their range of values for the older stations (Antananarivo, Anse Quitor, Mahé, and Saint-Denis). Ground-based measurements of the UVI are used to validate satellite estimates – Ozone Monitoring Instrument (OMI), the TROPOspheric Monitoring Instrument (TROPOMI), and the Global Ozone Monitoring Experiment (GOME) – and model forecasts of UVI – Tropospheric Emission Monitoring Internet Service (TEMIS) and Copernicus Atmospheric Monitoring Service (CAMS). The median relative differences between satellite or model estimates and ground-based measurements of clear-sky UVI range between −34.5 % and 15.8 %. Under clear skies, the smallest UVI median difference between the satellite or model estimates and the measurements made by ground-based instruments is found to be 0.02 (TROPOMI), 0.04 (OMI), −0.1 (CAMS), and −0.4 (CAMS) at Saint-Denis, Antananarivo, Anse Quitor, and Mahé, respectively. The diurnal variability in UVI and cloud fraction, as well as the monthly variability in UVI, is evaluated to ensure the quality of the dataset. The data used in this study are available at https://doi.org/10.5281/zenodo.4811488 (Lamy and Portafaix, 2021a).